Adding snippet for embedding visualisation

marimo/_snippets/data/altair-15.py

@@ -0,0 +1,74 @@
+# /// script
+# requires-python = ">=3.12"
+# dependencies = [
+#     "altair==5.5.0",
+#     "marimo",
+#     "model2vec==0.6.0",
+#     "polars==1.31.0",
+#     "scikit-learn==1.7.1",
+#     "umap-learn==0.5.9.post2",
+# ]
+# ///
+
+import marimo
+
+__generated_with = "0.14.13"
+app = marimo.App(width="columns")
+
+
+@app.cell
+def _(mo):
+    mo.md(
+        r"""
+        # Visualization: Embedding Summary and Bulk Selection 
+
+        Create interactive dashboards using `mo.altair_chart` and `UMAP`.
+        This technique is generally useful for any kind of embedding, but we're demonstrating it with text embeddings below.
+        """
+    )
+    return
+
+
+
+@app.cell
+def _():
+    import marimo as mo
+    import polars as pl 
+    import altair as alt
+    from model2vec import StaticModel
+    from umap import UMAP
+    return StaticModel, UMAP, alt, mo, pl
+
+
+@app.cell
+def _(StaticModel, UMAP, pl):
+    DATASET = "https://calmcode.io/static/data/clinc.csv"
+    TEXT_COL = "text"
+
+    df = pl.read_csv(DATASET).sample(10_000)
+
+    # We're using Model2Vec because it so lightweight, sentence-transformers will also work!
+    tfm = StaticModel.from_pretrained("minishlab/potion-base-8M")
+    df = df.with_columns(emb=tfm.encode(df[TEXT_COL].to_list()))
+
+    # UMAP turns the high-dimensional embeddings into 2D points which are easier to visualize.
+    x_pca = UMAP(n_components=2).fit_transform(df["emb"].to_numpy())
+    df = df.with_columns(x=x_pca[:, 0], y=x_pca[:, 1]).select(TEXT_COL, "x", "y")
+    return TEXT_COL, df
+
+
+@app.cell
+def _(alt, df, mo):
+    chart = mo.ui.altair_chart(alt.Chart(df).mark_point().encode(x="x", y="y").properties(width=500))
+    return (chart,)
+
+
+@app.cell
+def _(TEXT_COL, chart, mo):
+    mo.hstack([chart, chart.value.select(TEXT_COL)])
+    return
+
+
+if __name__ == "__main__":
+    app.run()
+